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+are+a+sample-efficient+method+for+learning+object-centric+robot+manipulation+tasks.+However%252C+there+are+several+open+challenges+to+applying+TP-GMMs+in+the+wild.+In+this+work%252C+we+tackle+three+crucial+challenges+synergistically.+First%252C+end-effector+velocities+are+non-Euclidean+and+thus+hard+to+model+using+standard+GMMs.+We+thus+propose+to+factorize+the+robot%27s+end-effector+velocity+into+its+direction+and+magnitude%252C+and+model+them+using+Riemannian+GMMs.+Second%252C+we+leverage+the+factorized+velocities+to+segment+and+sequence+skills+from+complex+demonstration+trajectories.+Through+the+segmentation%252C+we+further+align+skill+trajectories+and+hence+leverage+time+as+a+powerful+inductive+bias.+Third%252C+we+present+a+method+to+automatically+detect+relevant+task+parameters+per+skill+from+visual+observations.+Our+approach+enables+learning+complex+manipulation+tasks+from+just+five+demonstrations+while+using+only+RGB-D+observations.+Extensive+experimental+evaluations+on+RLBench+demonstrate+that+our+approach+achieves+state-of-the-art+performance+with+20-fold+improved+sample+efficiency.+Our+policies+generalize+across+different+environments%252C+object+instances%252C+and+object+positions%252C+while+the+learned+skills+are+reusable.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.cd61e24acea33ff5f87dff2e0b3cfe60%3frik%3dpdPrghNlHiYF1g%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Imitation+Learning+Manipulation&FORM=IRPRST&ck=C33D44C34EF36B9875025B8514CB1751&selectedIndex=4&itb=0)
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